Research On Fuzzy PID Automatic Driving Control And Digital System Of Wheeled Tunnel Equipment

At present,the walking control mode of tunnel equipment is still manual control of rail walking and tire walking.The longitudinal movement and precise laying of the track are extremely difficult.The manual positioning of tire walking equipment is very difficult.However,in the current intelligent research of tunnel equipment,there is little research on the automation and unmanned driving performance of equipment in tunnel environment.This thesis takes the tire walking tunnel equipment as the research object.The research on its autopilot control algorithm and data-driven digital system is carried out.The equipment kinematics model in tunnel environment is built,and the mathematical model of auto drive system based on fuzzy PID control algorithm is built.A data acquisition system based on Access database is designed.The differential steering control model is established by using Modbus communication frequency conversion control algorithm.A digital simulation test platform and experimental information platform for synchronous motion omni-directional monitoring equipment operation through data-driven model are created.The simulation analysis and experimental verification are carried out in a certain range of initial attitude parameters.Firstly,this thesis analyzes the requirements of the function of wheeled tunnel equipment on the walking and steering mechanism.After that,a motion system is established in which the wheels on both sides are independent of each other and driven by variable frequency motor to realize differential steering.In view of the slow running speed of wheeled tunnel equipment,large turning radius,small slope and rough concrete pavement of the tunnel.Based on the double track tunnel of high-speed railway,the absolute coordinate system for kinematic analysis of automatic driving is established.In this coordinate system,the equipment centroid kinematics equation is established with the wheel speeds n₁ and n₂ on both sides,the body deflection angle p’and the transverse displacement y of the centroid as the system variables.The distance variation function d_i（n₁,n₂,t）between the equipment outline and the tunnel gauge is derived.Secondly,the kinematic equation of the center of mass and the distance variation function between the equipment outline and the tunnel gauge are taken as the theoretical basis.Take the tunnel center line or the parallel line from the tunnel to the gauge as the target path.According to the fuzzy PID control algorithm,the motion control model of body deflection angle p and transverse displacement y of mass center is established.The body deflection angle deviation e₁ and the deviation change rate ec₁ are the input variables of the body deflection angle fuzzy PID control algorithm.The body deflection angle p′is the input variable of the speed compensation fuzzy coefficient control algorithm.The deflection angle of the motor in the model is accurately controlled by the processing of the variable speed of the motor.The centroid lateral displacement deviation e₂ and the deviation change rate ec₂ are the input variables of the centroid lateral displacement PID control algorithm.After the output variables are processed by the centroid lateral control model,the accurate control signal of motor speed to realize centroid alignment is obtained.Through the above fuzzy PID autopilot control algorithm,the center of mass and deflection angle are adjusted alternately.Then the automatic driving control of tunnel equipment is realized.Thirdly,with Lab VIEW,combined with kinematics model and fuzzy PID control algorithm,the digital simulation platform of automatic driving of tunnel equipment is designed in a modular way.The real platform consists of algorithm simulation and observation module,2D data visualization module,3D model motion module and data storage module.Visual 2D graphics and 3D models can be driven based on device motion data to move synchronously with the simulation algorithm model.The platform can predict whether there is a risk of colliding with the tunnel wall when starting from this attitude within 0.3s after the tunnel equipment is started.After setting a certain range of initial attitude parameters,the autopilot simulation analysis is carried out through the digital simulation platform.The simulation analysis results show that the test vehicle runs about 2.97m in the shortest time of 20 seconds,and can achieve the stable alignment self-driving state.The maximum transverse displacement y_max of the center of mass during the movement is 186mm.The maximum center of mass transverse fluctuation rangeΔy_max is about 196mm.The maximum lateral displacement y′_maxof the center of mass produced by steering is 136mm.Finally,the digital information system of the experimental vehicle is designed.It is designed according to the digital simulation analysis platform,data acquisition system,runway boundary detection algorithm and Modbus communication frequency conversion control algorithm.The information system and the experimental vehicle form an experimental test platform.Under the boundary conditions,the automatic driving test of the experimental vehicle with different initial attitude parameters is carried out.The results of motion state response show that the slowest driving speed of the experimental vehicle is 8m and takes 70s,which can achieve stable centering and automatic driving state.The maximum transverse displacement y_max of the center of mass during the movement is about 259mm.The maximum transverse fluctuation range of centroidΔy_max is about 279mm.The maximum lateral displacement y′_max of the center of mass produced by steering is 209mm.The data show that the transverse fluctuation amplitude and range of the center of mass are small when the experimental vehicle is running.The functions of digital information platform,such as model synchronous motion,have also been effectively verified.It shows that the autonomous driving control algorithm and digital information system studied in this thesis can solve the problems of automatic driving,anti-collision prediction and visual monitoring of the movement state of wheeled tunnel equipment in tunnel environment.It can provide a new theoretical support and technology for intelligent tunnel construction.